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[data] llama3 multi tool support (#8124)
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@ -53,9 +53,7 @@ Choose your path:
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- **Documentation**: https://llamafactory.readthedocs.io/en/latest/
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- **Colab (free)**: https://colab.research.google.com/drive/1eRTPn37ltBbYsISy9Aw2NuI2Aq5CQrD9?usp=sharing
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- **Local machine**: Please refer to [usage](#getting-started)
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- **PAI-DSW (free trial)**: [Llama3 Example](https://gallery.pai-ml.com/#/preview/deepLearning/nlp/llama_factory) | [Qwen2-VL Example](https://gallery.pai-ml.com/#/preview/deepLearning/nlp/llama_factory_qwen2vl) | [DeepSeek-R1-Distill Example](https://gallery.pai-ml.com/#/preview/deepLearning/nlp/llama_factory_deepseek_r1_distill_7b)
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- **Amazon SageMaker**: [Blog](https://aws.amazon.com/cn/blogs/machine-learning/how-apoidea-group-enhances-visual-information-extraction-from-banking-documents-with-multimodal-models-using-llama-factory-on-amazon-sagemaker-hyperpod/)
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- **Easy Dataset**: [Fine-tune on Synthetic Data](https://buaa-act.feishu.cn/wiki/GVzlwYcRFiR8OLkHbL6cQpYin7g)
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- **PAI-DSW (free trial)**: https://gallery.pai-ml.com/#/preview/deepLearning/nlp/llama_factory
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> [!NOTE]
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> Except for the above links, all other websites are unauthorized third-party websites. Please carefully use them.
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@ -107,7 +105,7 @@ Choose your path:
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- [How Apoidea Group enhances visual information extraction from banking documents with multimodal models using LLaMA-Factory on Amazon SageMaker HyperPod](https://aws.amazon.com/cn/blogs/machine-learning/how-apoidea-group-enhances-visual-information-extraction-from-banking-documents-with-multimodal-models-using-llama-factory-on-amazon-sagemaker-hyperpod/) (English)
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- [Easy Dataset × LLaMA Factory: Enabling LLMs to Efficiently Learn Domain Knowledge](https://buaa-act.feishu.cn/wiki/GVzlwYcRFiR8OLkHbL6cQpYin7g) (English)
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- [LLaMA Factory: Fine-tuning the DeepSeek-R1-Distill-Qwen-7B Model For News Classifier](https://gallery.pai-ml.com/#/preview/deepLearning/nlp/llama_factory_deepseek_r1_distill_7b) (Chinese)
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- [LLaMA Factory: Fine-tuning the DeepSeek-R1-Distill-Qwen-7B Model for News Classifier](https://gallery.pai-ml.com/#/preview/deepLearning/nlp/llama_factory_deepseek_r1_distill_7b) (Chinese)
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<details><summary>All Blogs</summary>
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@ -918,6 +916,7 @@ If you have a project that should be incorporated, please contact via email or c
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1. **[RAG-Retrieval](https://github.com/NLPJCL/RAG-Retrieval)**: A full pipeline for RAG retrieval model fine-tuning, inference, and distillation. [[blog]](https://zhuanlan.zhihu.com/p/987727357)
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1. **[360-LLaMA-Factory](https://github.com/Qihoo360/360-LLaMA-Factory)**: A modified library that supports long sequence SFT & DPO using ring attention.
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1. **[Sky-T1](https://novasky-ai.github.io/posts/sky-t1/)**: An o1-like model fine-tuned by NovaSky AI with very small cost.
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1. **[WeClone](https://github.com/xming521/WeClone)**: One-stop solution for creating your digital avatar from chat logs.
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</details>
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@ -55,9 +55,7 @@ https://github.com/user-attachments/assets/43b700c6-a178-41db-b1f8-8190a5d3fcfc
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- **框架文档（昇腾 NPU）**：https://ascend.github.io/docs/sources/llamafactory/
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- **Colab（免费）**：https://colab.research.google.com/drive/1d5KQtbemerlSDSxZIfAaWXhKr30QypiK?usp=sharing
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- **本地机器**：请见[如何使用](#如何使用)
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- **PAI-DSW（免费试用）**：[Llama3 案例](https://gallery.pai-ml.com/#/preview/deepLearning/nlp/llama_factory) | [Qwen2-VL 案例](https://gallery.pai-ml.com/#/preview/deepLearning/nlp/llama_factory_qwen2vl) | [DeepSeek-R1-Distill 案例](https://gallery.pai-ml.com/#/preview/deepLearning/nlp/llama_factory_deepseek_r1_distill_7b)
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- **Amazon SageMaker**：[博客](https://aws.amazon.com/cn/blogs/china/a-one-stop-code-free-model-fine-tuning-deployment-platform-based-on-sagemaker-and-llama-factory/)
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- **Easy Dataset**：[数据蒸馏微调](https://buaa-act.feishu.cn/wiki/KY9xwTGs1iqHrRkjXBwcZP9WnL9)
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- **PAI-DSW（免费试用）**：https://gallery.pai-ml.com/#/preview/deepLearning/nlp/llama_factory
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> [!NOTE]
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> 除上述链接以外的其他网站均为未经许可的第三方网站，请小心甄别。
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@ -109,12 +107,12 @@ https://github.com/user-attachments/assets/43b700c6-a178-41db-b1f8-8190a5d3fcfc
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- [通过亚马逊 SageMaker HyperPod 上的 LLaMA-Factory 增强多模态模型银行文档的视觉信息提取](https://aws.amazon.com/cn/blogs/machine-learning/how-apoidea-group-enhances-visual-information-extraction-from-banking-documents-with-multimodal-models-using-llama-factory-on-amazon-sagemaker-hyperpod/)（英文）
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- [Easy Dataset × LLaMA Factory: 让大模型高效学习领域知识](https://buaa-act.feishu.cn/wiki/KY9xwTGs1iqHrRkjXBwcZP9WnL9)（中文）
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- [LLaMA Factory：微调DeepSeek-R1-Distill-Qwen-7B模型实现新闻标题分类器](https://gallery.pai-ml.com/#/preview/deepLearning/nlp/llama_factory_deepseek_r1_distill_7b)（中文）
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- [LLaMA Factory：微调 DeepSeek-R1-Distill-Qwen-7B 模型实现新闻标题分类器](https://gallery.pai-ml.com/#/preview/deepLearning/nlp/llama_factory_deepseek_r1_distill_7b)（中文）
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<details><summary>全部博客</summary>
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- [基于 Amazon SageMaker 和 LLaMA-Factory 打造一站式无代码模型微调部署平台 Model Hub](https://aws.amazon.com/cn/blogs/china/a-one-stop-code-free-model-fine-tuning-deployment-platform-based-on-sagemaker-and-llama-factory/)（中文）
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- [LLaMA Factory多模态微调实践：微调Qwen2-VL构建文旅大模型](https://gallery.pai-ml.com/#/preview/deepLearning/nlp/llama_factory_qwen2vl)（中文）
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- [LLaMA Factory 多模态微调实践：微调 Qwen2-VL 构建文旅大模型](https://gallery.pai-ml.com/#/preview/deepLearning/nlp/llama_factory_qwen2vl)（中文）
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- [LLaMA Factory：微调LLaMA3模型实现角色扮演](https://gallery.pai-ml.com/#/preview/deepLearning/nlp/llama_factory)（中文）
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</details>
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@ -920,6 +918,7 @@ swanlab_run_name: test_run # 可选
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1. **[RAG-Retrieval](https://github.com/NLPJCL/RAG-Retrieval)**：一个全链路 RAG 检索模型微调、推理和蒸馏代码库。[[blog]](https://zhuanlan.zhihu.com/p/987727357)
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1. **[360-LLaMA-Factory](https://github.com/Qihoo360/360-LLaMA-Factory)**：一个魔改后的代码库，通过 Ring Attention 支持长序列的 SFT 和 DPO 训练。
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1. **[Sky-T1](https://novasky-ai.github.io/posts/sky-t1/)**：由 NovaSky AI 微调的低成本类 o1 长推理模型。
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1. **[WeClone](https://github.com/xming521/WeClone)**：从聊天记录创造数字分身的一站式解决方案。
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</details>
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@ -125,11 +125,7 @@ class DefaultToolUtils(ToolUtils):
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@override
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@staticmethod
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def function_formatter(functions: list["FunctionCall"]) -> str:
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function_text = ""
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for name, arguments in functions:
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function_text += f"Action: {name}\nAction Input: {arguments}\n"
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return function_text
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return "\n".join([f"Action: {name}\nAction Input: {arguments}" for name, arguments in functions])
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@override
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@staticmethod
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@ -210,24 +206,23 @@ class Llama3ToolUtils(ToolUtils):
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@override
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@staticmethod
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def function_formatter(functions: list["FunctionCall"]) -> str:
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if len(functions) > 1:
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raise ValueError("Llama-3 does not support parallel functions.")
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return f'{{"name": "{functions[0].name}", "parameters": {functions[0].arguments}}}'
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function_objects = [{"name": name, "parameters": json.loads(arguments)} for name, arguments in functions]
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return json.dumps(function_objects[0] if len(function_objects) == 1 else function_objects, ensure_ascii=False)
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@override
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@staticmethod
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def tool_extractor(content: str) -> Union[str, list["FunctionCall"]]:
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try:
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tool = json.loads(content.strip())
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tools = json.loads(content.strip())
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except json.JSONDecodeError:
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return content
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if "name" not in tool or "parameters" not in tool:
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tools = [tools] if not isinstance(tools, list) else tools
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try:
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return [FunctionCall(tool["name"], json.dumps(tool["parameters"], ensure_ascii=False)) for tool in tools]
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except KeyError:
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return content
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return [FunctionCall(tool["name"], json.dumps(tool["parameters"], ensure_ascii=False))]
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class MistralToolUtils(ToolUtils):
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r"""Mistral v0.3 tool using template."""
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@ -244,11 +239,9 @@ class MistralToolUtils(ToolUtils):
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@override
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@staticmethod
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def function_formatter(functions: list["FunctionCall"]) -> str:
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function_texts = []
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for name, arguments in functions:
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function_texts.append(f'{{"name": "{name}", "arguments": {arguments}}}')
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return "[" + ", ".join(function_texts) + "]"
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return json.dumps(
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[{"name": name, "arguments": json.loads(arguments)} for name, arguments in functions], ensure_ascii=False
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)
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@override
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@staticmethod
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@ -258,17 +251,11 @@ class MistralToolUtils(ToolUtils):
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except json.JSONDecodeError:
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return content
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if not isinstance(tools, list):
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tools = [tools]
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results = []
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for tool in tools:
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if "name" not in tool or "arguments" not in tool:
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return content
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results.append(FunctionCall(tool["name"], json.dumps(tool["arguments"], ensure_ascii=False)))
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return results
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tools = [tools] if not isinstance(tools, list) else tools
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try:
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return [FunctionCall(tool["name"], json.dumps(tool["arguments"], ensure_ascii=False)) for tool in tools]
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except KeyError:
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return content
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class QwenToolUtils(ToolUtils):
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@ -287,13 +274,11 @@ class QwenToolUtils(ToolUtils):
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@override
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@staticmethod
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def function_formatter(functions: list["FunctionCall"]) -> str:
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function_texts = []
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for name, arguments in functions:
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function_texts.append(
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"<tool_call>\n" + f'{{"name": "{name}", "arguments": {arguments}}}' + "\n</tool_call>"
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)
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return "\n".join(function_texts)
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function_texts = [
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json.dumps({"name": name, "arguments": json.loads(arguments)}, ensure_ascii=False)
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for name, arguments in functions
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]
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return "\n".join([f"<tool_call>\n{text}\n</tool_call>" for text in function_texts])
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@override
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@staticmethod
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@ -50,7 +50,7 @@ def test_function_formatter():
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formatter = FunctionFormatter(slots=["{{content}}", "</s>"], tool_format="default")
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tool_calls = json.dumps(FUNCTION)
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assert formatter.apply(content=tool_calls) == [
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"""Action: tool_name\nAction Input: {"foo": "bar", "size": 10}\n""",
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"""Action: tool_name\nAction Input: {"foo": "bar", "size": 10}""",
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"</s>",
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]
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@ -60,7 +60,7 @@ def test_multi_function_formatter():
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tool_calls = json.dumps([FUNCTION] * 2)
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assert formatter.apply(content=tool_calls) == [
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"""Action: tool_name\nAction Input: {"foo": "bar", "size": 10}\n"""
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"""Action: tool_name\nAction Input: {"foo": "bar", "size": 10}\n""",
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"""Action: tool_name\nAction Input: {"foo": "bar", "size": 10}""",
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"</s>",
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]
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@ -85,7 +85,7 @@ def test_default_tool_formatter():
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def test_default_tool_extractor():
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formatter = ToolFormatter(tool_format="default")
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result = """Action: test_tool\nAction Input: {"foo": "bar", "size": 10}\n"""
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result = """Action: test_tool\nAction Input: {"foo": "bar", "size": 10}"""
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assert formatter.extract(result) == [("test_tool", """{"foo": "bar", "size": 10}""")]
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@ -93,7 +93,7 @@ def test_default_multi_tool_extractor():
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formatter = ToolFormatter(tool_format="default")
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result = (
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"""Action: test_tool\nAction Input: {"foo": "bar", "size": 10}\n"""
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"""Action: another_tool\nAction Input: {"foo": "job", "size": 2}\n"""
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"""Action: another_tool\nAction Input: {"foo": "job", "size": 2}"""
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)
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assert formatter.extract(result) == [
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("test_tool", """{"foo": "bar", "size": 10}"""),
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@ -125,12 +125,22 @@ def test_glm4_tool_extractor():
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def test_llama3_function_formatter():
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formatter = FunctionFormatter(slots=["{{content}}<|eot_id|>"], tool_format="llama3")
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tool_calls = json.dumps({"name": "tool_name", "arguments": {"foo": "bar", "size": 10}})
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tool_calls = json.dumps(FUNCTION)
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assert formatter.apply(content=tool_calls) == [
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"""{"name": "tool_name", "parameters": {"foo": "bar", "size": 10}}<|eot_id|>"""
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]
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def test_llama3_multi_function_formatter():
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formatter = FunctionFormatter(slots=["{{content}}<|eot_id|>"], tool_format="llama3")
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tool_calls = json.dumps([FUNCTION] * 2)
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assert formatter.apply(content=tool_calls) == [
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"""[{"name": "tool_name", "parameters": {"foo": "bar", "size": 10}}, """
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"""{"name": "tool_name", "parameters": {"foo": "bar", "size": 10}}]"""
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"""<|eot_id|>"""
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]
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def test_llama3_tool_formatter():
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formatter = ToolFormatter(tool_format="llama3")
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date = datetime.now().strftime("%d %b %Y")
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@ -150,6 +160,18 @@ def test_llama3_tool_extractor():
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assert formatter.extract(result) == [("test_tool", """{"foo": "bar", "size": 10}""")]
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def test_llama3_multi_tool_extractor():
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formatter = ToolFormatter(tool_format="llama3")
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result = (
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"""[{"name": "test_tool", "parameters": {"foo": "bar", "size": 10}}, """
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"""{"name": "another_tool", "parameters": {"foo": "job", "size": 2}}]"""
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)
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assert formatter.extract(result) == [
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("test_tool", """{"foo": "bar", "size": 10}"""),
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("another_tool", """{"foo": "job", "size": 2}"""),
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]
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def test_mistral_function_formatter():
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formatter = FunctionFormatter(slots=["[TOOL_CALLS] {{content}}", "</s>"], tool_format="mistral")
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tool_calls = json.dumps(FUNCTION)
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